neurodudes

I came across this fantastic review of tools for the Genetic Dissection of Neural Circuits in Neuron a few days ago. It’s by Liqun Luo, Ed Callaway, and Karel Svoboda. I highly recommend it, as it spans the gamut from genetic targeting (recombination, binary logic, viral delivery) to circuit reconstruction (super resolution LM, EM, brainbow) to activity modulation and functional mapping (uncaging, artificial GPCRs, light-gated channels, MIST). I don’t think I’ve ever seen quite a review of so many cutting edge neurotechnologies in one place. I can’t recommend this piece enough really. For me, with my lack of molecular expertise, the first sections on combinatorial gene targeting/expression techniques were great, pulling together Gal4, Cre/Flp, and Tet systems into a unified framework, along with more general concepts like site-directed integration, enhancer-trap, and repressor trap (eg. Thy1 mice).

Rewiring the Brain: Inside the New Science of Neuroengineering.

Interviews Boyden and Deisseroth. Follow the link a video of an optogenetically controlled mouse.

Nature methods has a small piece interviewing Karl Deisseroth on properties of the new step ChR2.

Some shortcomings of step ChR2 and future research directions:

Deisseroth expects ongoing efforts to improve key features of these channels. “One disadvantage is that some of the mutants have reduced current compared to wild type, so multiple mutations may help to bring those current levels back up to wild-type levels,” he says. Projects designed to improve membrane targeting and to apply a composite of opsins, including the red light–responsive channelrhodopsin from Volvox carteri, are also in the works in his laboratory.

As has become a hallmark of the Svoboda lab, this new paper in Nature (advance online publication) combines several cutting edge technologies (rAAV-delivered ChR2, most prominently, and 2-photon 1-photon laser stimulation) to do some interesting synaptic physiology.

The subcellular organization of neocortical excitatory connections : Article : Nature.

They used ChR2 (with TTX and 4-AP to block action potentials) to find where on the dendritic tree particular inputs synapsed onto L3 and L5 cells and to measure the strength of those inputs. ChR2 depolarizes the input axon locally (60um spot diameter) at points of (potential) axodendritic contact. If you’ve heard the term “potential synapse” before, then think of this technique as a way of checking potential synapses and seeing if there really is an actual synapse there.

The technique allowed them to map on a L3 barrel cortex pyramidal cell where different thalamic inputs (VPm, POm) and cortical inputs (M1, barrel L2/3, barrel L4):

sCRACM stands for subcellular ChR2-assisted circuit mapping.

PNAS has some interesting articles that I came across today:

1. 3D PALM (open access): Using 2-photon and photoactivatable proteins, the authors image beyond the usual sub-wavelength TIRF limits. They image over multiple microns with 50nm resolution.
2. Neuroeconomics:  Low digit ratio (2d:4d) predicts financial success in traders. Okay, measure the length of your index and ring fingers. (Not sure if this analysis applies for the ladies; the authors only used men in the study.) Calculate the ratio (2d/4d); longer ring fingers signify greater fetal androgen exposure. The mean value is about 0.96. As the authors say,
   

   Digit ratios have been found to predict performance in competitive sports, such as soccer, rugby, basketball, and skiing, so 2D:4D may also predict the risk preferences and physical speed required for high-frequency trading.

   A strong correlation (r~0.5) was found between low digit ratios and profits in short-term trading. So, they take on more risk and make more money. What I want to know is how well the low 2d:4d ratio traders did over the last 6 months!

Nature is reporting on potential flaw in multiple imaging (fMRI) studies of social neuroscience. Ed Vul (a graduate student in my dept) and colleagues have a paper in press that says that many of the high correlations between brain regions and social behavior are implausible, given the inherent variability/noise in fMRI. Furthermore, based on a survey of methods from individual investigators, they created a list of papers that commit, in their view, a statistical mistake (non-independence). Naturally, the authors named in the paper aren’t happy and, according to the Nature article, several rebuttals are in the works. At the very least, to my non-expert eyes, this seems like an important discussion to have about data analysis and methodology.

Bi-stable neural state switches : Article : Nature Neuroscience

Another channelrhodopsin breakthrough from Deisseroth’s lab. This time light is not required to keep the channel open. Light merely triggers opening and closing behavior. Blue-shifted light opens channels and red-shifted light closes them. This looks like another potentially powerful neurotechnology for interrogating circuits and systems.

Relevant fig:

The Circadian Clock in the Retina Controls Rod-Cone Coupling (Christophe Ribelayga, Yu Cao, and Stuart C. Mangel)

An amazing paper from Neuron demonstrating adaptive (circadian clock-governed) binning in the retina, based on dopamine modulation of gap junction (electrical) synapses between retinal photodetectors. During the day, abundant dopamine release weakens gap junctions coupling rods and cones together so that visual acuity is high. When light is scarce (at night), there is less dopamine and the electrical coupling between rods and cones is increased. This is analogous to on-chip binning in CCD (digital) cameras. Binning increases signal (in light-limited systems, eg. seeing at night) by increasing optical input area and by reducing single element noise (ie. noise at different photoreceptors should be independent) at the cost of resolution. So, the retina activates photoreceptor binning at night to boost low-light signals and deactivates it during the day to increase resolution. The dopamine comes from cells in the interplexiform layer, whose dopamine release is itself governed by melatonin projections.

Also, I never knew that gap junction strengths were directly modifiable. It looks like the D2 receptors are G-protein coupled to PKA, which acts on the gap junctions.

There has been a few articles recently in the NYT about the neural mechanisms used by mosquito repellents. What a wonderful idea: Do some ephys recordings to find which neurons are sensitive to DEET (the current standard for mosquito repellents, which I can attest both doesn’t work very well and eats holes in synthetic clothing) and then build targeted compounds for those receptors/neurons/pathways. I always like this type of simple and practical neuroengineering.

Right now, it appears that there’s a bit of controversy in the field. Earlier this year, in Science, a group from Rockefeller found that DEET masked sensitivity to human odors by interfering with a particular odorant receptor. This impressive result was recently question by entomologists from UC-Davis in a PNAS paper claiming that DEET acts directly on a particular olfactory receptor neuron and does not attenuate the response to the same human-emitted odorant, as found in the earlier paper. Although the results appear to be conflicting, the studies use different techniques and thus it is likely that DEET’s action might be more complex than either paper claims. Still, the idea of identifying a target for chemical intervention by looking at electrophysiological responses to DEET is smart.

In related work, earlier this year a group from Colorado State University, as described in this PNAS overview, “conducted a rigorous search of a library of N-acylpiperidines, using an artificial neural network to identify strong candidates, and then tested them in the laboratory on human volunteers.” They found a candidate molecule that has a ~4X longer repellency effect than DEET. Here’s a photo from the experiments (DEET vs. untreated hand)… ouch!

Plants Found to Show Preferences for Their Relatives – NYTimes.com

Two amazing things here:

1. Plants missing photosynthetic enzymes of their own that migrate directionally toward “victim” plants. This behavior has an uncanny resemblance to axon guidance. Make sure to view the time-lapse video in the NYT article. Here’s an image from the PSU website:
   
   
2. Plants capable of identifying kin and “being nice” to kin while going into a competitive mode of root growth with non-kin. Amazing.

It refreshing to see this kind of interesting behavior without any neurons involved. It makes me think (realize) that the idea of a neuron or a neural system has many components and there might not be any good reason to assume that a single cell must have all of those properties or none of them. Something like a neuron-like cell that’s not a neuron in the classical sense. Anyone know of other examples?